Overview

Dataset statistics

Number of variables8
Number of observations12744
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory796.6 KiB
Average record size in memory64.0 B

Variable types

Numeric8

Alerts

per_area_buildings is highly overall correlated with per_area_greenery and 2 other fieldsHigh correlation
per_area_greenery is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
per_residential_road is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
per_rural_road is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
publictransport_frequency has 3691 (29.0%) zerosZeros
per_area_greenery has 128 (1.0%) zerosZeros
per_area_water has 1441 (11.3%) zerosZeros
per_residential_road has 612 (4.8%) zerosZeros
per_rural_road has 4550 (35.7%) zerosZeros
per_highway has 10771 (84.5%) zerosZeros
per_active has 584 (4.6%) zerosZeros

Reproduction

Analysis started2024-07-05 14:31:49.567767
Analysis finished2024-07-05 14:32:17.366339
Duration27.8 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

publictransport_frequency
Real number (ℝ)

ZEROS 

Distinct3508
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1341.5055
Minimum0
Maximum93916
Zeros3691
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size99.7 KiB
2024-07-05T16:32:17.798245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median379.5
Q31363.25
95-th percentile5608.55
Maximum93916
Range93916
Interquartile range (IQR)1363.25

Descriptive statistics

Standard deviation3147.7508
Coefficient of variation (CV)2.3464316
Kurtosis156.52784
Mean1341.5055
Median Absolute Deviation (MAD)379.5
Skewness9.0008477
Sum17096146
Variance9908335.4
MonotonicityNot monotonic
2024-07-05T16:32:18.283488image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3691
29.0%
48 31
 
0.2%
144 29
 
0.2%
528 27
 
0.2%
44 27
 
0.2%
264 25
 
0.2%
84 25
 
0.2%
80 23
 
0.2%
136 23
 
0.2%
72 22
 
0.2%
Other values (3498) 8821
69.2%
ValueCountFrequency (%)
0 3691
29.0%
2 7
 
0.1%
3 2
 
< 0.1%
4 10
 
0.1%
5 2
 
< 0.1%
6 3
 
< 0.1%
7 4
 
< 0.1%
8 7
 
0.1%
10 14
 
0.1%
11 10
 
0.1%
ValueCountFrequency (%)
93916 1
< 0.1%
83473 1
< 0.1%
72615 1
< 0.1%
52370 1
< 0.1%
49806 1
< 0.1%
49779 1
< 0.1%
45294 1
< 0.1%
43720 1
< 0.1%
39980 1
< 0.1%
37190 1
< 0.1%

per_area_greenery
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12617
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.004273
Minimum0
Maximum38.2371
Zeros128
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size99.7 KiB
2024-07-05T16:32:18.767581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.98353138
Q14.5944091
median9.3070449
Q321.081485
95-th percentile32.261449
Maximum38.2371
Range38.2371
Interquartile range (IQR)16.487076

Descriptive statistics

Standard deviation10.345587
Coefficient of variation (CV)0.79555289
Kurtosis-0.87182144
Mean13.004273
Median Absolute Deviation (MAD)6.1531337
Skewness0.69075313
Sum165726.46
Variance107.03117
MonotonicityNot monotonic
2024-07-05T16:32:19.684150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 128
 
1.0%
0.6084135957 1
 
< 0.1%
14.39051884 1
 
< 0.1%
14.72944416 1
 
< 0.1%
12.70209476 1
 
< 0.1%
8.068993149 1
 
< 0.1%
5.674481713 1
 
< 0.1%
3.419192051 1
 
< 0.1%
0.6928398073 1
 
< 0.1%
24.96979283 1
 
< 0.1%
Other values (12607) 12607
98.9%
ValueCountFrequency (%)
0 128
1.0%
3.311726792 × 10-51
 
< 0.1%
0.0002723611374 1
 
< 0.1%
0.0004865381444 1
 
< 0.1%
0.001162506368 1
 
< 0.1%
0.001353928494 1
 
< 0.1%
0.001474690544 1
 
< 0.1%
0.001575551507 1
 
< 0.1%
0.001637908808 1
 
< 0.1%
0.002748089653 1
 
< 0.1%
ValueCountFrequency (%)
38.23710024 1
< 0.1%
37.04451075 1
< 0.1%
36.45912947 1
< 0.1%
36.38465915 1
< 0.1%
36.30877408 1
< 0.1%
36.2772134 1
< 0.1%
36.26314952 1
< 0.1%
36.20298637 1
< 0.1%
36.12396057 1
< 0.1%
36.1213846 1
< 0.1%

per_area_water
Real number (ℝ)

ZEROS 

Distinct11304
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2849437
Minimum0
Maximum27.855333
Zeros1441
Zeros (%)11.3%
Negative0
Negative (%)0.0%
Memory size99.7 KiB
2024-07-05T16:32:20.333861image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13275557
median0.7049448
Q31.8034898
95-th percentile4.3324059
Maximum27.855333
Range27.855333
Interquartile range (IQR)1.6707343

Descriptive statistics

Standard deviation1.776736
Coefficient of variation (CV)1.3827345
Kurtosis22.83698
Mean1.2849437
Median Absolute Deviation (MAD)0.6681613
Skewness3.5942775
Sum16375.322
Variance3.1567908
MonotonicityNot monotonic
2024-07-05T16:32:20.850988image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1441
 
11.3%
1.822359097 1
 
< 0.1%
0.5088225413 1
 
< 0.1%
2.308456288 1
 
< 0.1%
1.187170772 1
 
< 0.1%
0.9771659773 1
 
< 0.1%
0.5600370815 1
 
< 0.1%
8.030660101 1
 
< 0.1%
0.6868084079 1
 
< 0.1%
0.3547577527 1
 
< 0.1%
Other values (11294) 11294
88.6%
ValueCountFrequency (%)
0 1441
11.3%
2.632667253 × 10-91
 
< 0.1%
4.321416001 × 10-61
 
< 0.1%
3.323163681 × 10-51
 
< 0.1%
4.429612964 × 10-51
 
< 0.1%
5.712498559 × 10-51
 
< 0.1%
0.0001076551272 1
 
< 0.1%
0.0001089278906 1
 
< 0.1%
0.0001393320081 1
 
< 0.1%
0.0001555083643 1
 
< 0.1%
ValueCountFrequency (%)
27.85533324 1
< 0.1%
20.68321406 1
< 0.1%
20.18943585 1
< 0.1%
20.10695869 1
< 0.1%
19.89993472 1
< 0.1%
19.24829146 1
< 0.1%
19.19504426 1
< 0.1%
18.80182505 1
< 0.1%
18.62419928 1
< 0.1%
17.40978801 1
< 0.1%

per_area_buildings
Real number (ℝ)

HIGH CORRELATION 

Distinct12741
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7466887
Minimum0
Maximum23.944334
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size99.7 KiB
2024-07-05T16:32:21.222851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21297425
Q11.2867477
median4.7781381
Q37.0170588
95-th percentile11.0475
Maximum23.944334
Range23.944334
Interquartile range (IQR)5.7303111

Descriptive statistics

Standard deviation3.5798329
Coefficient of variation (CV)0.75417479
Kurtosis0.43724247
Mean4.7466887
Median Absolute Deviation (MAD)2.7422594
Skewness0.66846895
Sum60491.801
Variance12.815204
MonotonicityNot monotonic
2024-07-05T16:32:21.850203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
< 0.1%
8.402089864 1
 
< 0.1%
6.488725528 1
 
< 0.1%
5.832055333 1
 
< 0.1%
4.513897941 1
 
< 0.1%
0.325635315 1
 
< 0.1%
5.076158301 1
 
< 0.1%
3.483155597 1
 
< 0.1%
5.617513435 1
 
< 0.1%
5.461553373 1
 
< 0.1%
Other values (12731) 12731
99.9%
ValueCountFrequency (%)
0 4
< 0.1%
0.001293250695 1
 
< 0.1%
0.00346380894 1
 
< 0.1%
0.004593450547 1
 
< 0.1%
0.005046682417 1
 
< 0.1%
0.005332112869 1
 
< 0.1%
0.006402565114 1
 
< 0.1%
0.006660210555 1
 
< 0.1%
0.007209041124 1
 
< 0.1%
0.009291787888 1
 
< 0.1%
ValueCountFrequency (%)
23.94433391 1
< 0.1%
20.90929894 1
< 0.1%
20.73901526 1
< 0.1%
20.71522834 1
< 0.1%
20.52081539 1
< 0.1%
20.44454525 1
< 0.1%
20.27873077 1
< 0.1%
20.17997013 1
< 0.1%
19.900095 1
< 0.1%
19.8617785 1
< 0.1%

per_residential_road
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11969
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.250523
Minimum0
Maximum100
Zeros612
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size99.7 KiB
2024-07-05T16:32:22.451084image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.007059939
Q129.842249
median69.057393
Q382.392597
95-th percentile95.987594
Maximum100
Range100
Interquartile range (IQR)52.550348

Descriptive statistics

Standard deviation32.496665
Coefficient of variation (CV)0.56762216
Kurtosis-0.97652073
Mean57.250523
Median Absolute Deviation (MAD)17.494207
Skewness-0.67815132
Sum729600.66
Variance1056.0332
MonotonicityNot monotonic
2024-07-05T16:32:22.966093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 612
 
4.8%
100 165
 
1.3%
88.4077739 1
 
< 0.1%
0.7603051424 1
 
< 0.1%
55.84471982 1
 
< 0.1%
36.72231928 1
 
< 0.1%
68.46464439 1
 
< 0.1%
3.666903535 1
 
< 0.1%
76.52573617 1
 
< 0.1%
5.340868931 1
 
< 0.1%
Other values (11959) 11959
93.8%
ValueCountFrequency (%)
0 612
4.8%
1.525622741 × 10-111
 
< 0.1%
3.311907826 × 10-61
 
< 0.1%
1.163882783 × 10-51
 
< 0.1%
1.466086327 × 10-51
 
< 0.1%
2.165094666 × 10-51
 
< 0.1%
0.0002431723272 1
 
< 0.1%
0.0007128453116 1
 
< 0.1%
0.0008332321941 1
 
< 0.1%
0.0008469049215 1
 
< 0.1%
ValueCountFrequency (%)
100 165
1.3%
99.9991583 1
 
< 0.1%
99.99613823 1
 
< 0.1%
99.9961159 1
 
< 0.1%
99.99557189 1
 
< 0.1%
99.98909053 1
 
< 0.1%
99.98842591 1
 
< 0.1%
99.98763712 1
 
< 0.1%
99.98614642 1
 
< 0.1%
99.97739784 1
 
< 0.1%

per_rural_road
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8126
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.444831
Minimum0
Maximum100
Zeros4550
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size99.7 KiB
2024-07-05T16:32:23.515058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.0423426
Q337.95282
95-th percentile83.762291
Maximum100
Range100
Interquartile range (IQR)37.95282

Descriptive statistics

Standard deviation29.354582
Coefficient of variation (CV)1.3688418
Kurtosis0.034297154
Mean21.444831
Median Absolute Deviation (MAD)5.0423426
Skewness1.2075064
Sum273292.93
Variance861.69146
MonotonicityNot monotonic
2024-07-05T16:32:23.981872image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4550
35.7%
100 70
 
0.5%
95.92179327 1
 
< 0.1%
14.76812925 1
 
< 0.1%
18.70284818 1
 
< 0.1%
2.782858884 1
 
< 0.1%
66.26601532 1
 
< 0.1%
11.63679785 1
 
< 0.1%
59.23006881 1
 
< 0.1%
6.937266567 1
 
< 0.1%
Other values (8116) 8116
63.7%
ValueCountFrequency (%)
0 4550
35.7%
1.12899023 × 10-61
 
< 0.1%
2.236974119 × 10-61
 
< 0.1%
2.5720649 × 10-61
 
< 0.1%
4.690270087 × 10-61
 
< 0.1%
6.099494146 × 10-61
 
< 0.1%
9.763297334 × 10-61
 
< 0.1%
1.679501575 × 10-51
 
< 0.1%
2.601461789 × 10-51
 
< 0.1%
4.019668254 × 10-51
 
< 0.1%
ValueCountFrequency (%)
100 70
0.5%
99.99636993 1
 
< 0.1%
99.99431241 1
 
< 0.1%
99.95623249 1
 
< 0.1%
99.94573691 1
 
< 0.1%
99.88647299 1
 
< 0.1%
99.84843166 1
 
< 0.1%
99.83385964 1
 
< 0.1%
99.81946486 1
 
< 0.1%
99.81624502 1
 
< 0.1%

per_highway
Real number (ℝ)

ZEROS 

Distinct1974
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2349268
Minimum0
Maximum82.433961
Zeros10771
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size99.7 KiB
2024-07-05T16:32:24.362227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile17.398169
Maximum82.433961
Range82.433961
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.9904132
Coefficient of variation (CV)3.1278042
Kurtosis20.573032
Mean2.2349268
Median Absolute Deviation (MAD)0
Skewness4.1049284
Sum28481.907
Variance48.865877
MonotonicityNot monotonic
2024-07-05T16:32:25.009650image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10771
84.5%
13.9767427 1
 
< 0.1%
14.56606935 1
 
< 0.1%
17.37956814 1
 
< 0.1%
13.91347285 1
 
< 0.1%
4.921794519 1
 
< 0.1%
3.007996178 1
 
< 0.1%
3.408974749 1
 
< 0.1%
28.27952794 1
 
< 0.1%
21.25808488 1
 
< 0.1%
Other values (1964) 1964
 
15.4%
ValueCountFrequency (%)
0 10771
84.5%
1.442931179 × 10-51
 
< 0.1%
2.687540288 × 10-51
 
< 0.1%
3.029249664 × 10-51
 
< 0.1%
6.660299538 × 10-51
 
< 0.1%
0.0007485863921 1
 
< 0.1%
0.003285777042 1
 
< 0.1%
0.007927801435 1
 
< 0.1%
0.008323287251 1
 
< 0.1%
0.009346152379 1
 
< 0.1%
ValueCountFrequency (%)
82.43396086 1
< 0.1%
80.15755522 1
< 0.1%
79.74481498 1
< 0.1%
79.10347365 1
< 0.1%
67.51655804 1
< 0.1%
62.64246933 1
< 0.1%
60.67138954 1
< 0.1%
59.89573277 1
< 0.1%
56.20348838 1
< 0.1%
55.96623381 1
< 0.1%

per_active
Real number (ℝ)

ZEROS 

Distinct12161
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.046179
Minimum0
Maximum86.75691
Zeros584
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size99.7 KiB
2024-07-05T16:32:25.631692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.080100451
Q19.1345143
median17.792413
Q326.777004
95-th percentile42.74823
Maximum86.75691
Range86.75691
Interquartile range (IQR)17.642489

Descriptive statistics

Standard deviation13.152217
Coefficient of variation (CV)0.69054359
Kurtosis1.1042409
Mean19.046179
Median Absolute Deviation (MAD)8.8164015
Skewness0.83893644
Sum242724.51
Variance172.98081
MonotonicityNot monotonic
2024-07-05T16:32:26.264323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 584
 
4.6%
11.5922261 1
 
< 0.1%
9.788170998 1
 
< 0.1%
9.531019825 1
 
< 0.1%
44.15528018 1
 
< 0.1%
4.639135942 1
 
< 0.1%
31.53535561 1
 
< 0.1%
70.41542188 1
 
< 0.1%
21.71429347 1
 
< 0.1%
33.3557686 1
 
< 0.1%
Other values (12151) 12151
95.3%
ValueCountFrequency (%)
0 584
4.6%
0.0008417035312 1
 
< 0.1%
0.001704609463 1
 
< 0.1%
0.003289480218 1
 
< 0.1%
0.003630066464 1
 
< 0.1%
0.003861769906 1
 
< 0.1%
0.003884102085 1
 
< 0.1%
0.004428113489 1
 
< 0.1%
0.00618426505 1
 
< 0.1%
0.006435347207 1
 
< 0.1%
ValueCountFrequency (%)
86.75691032 1
< 0.1%
86.41354275 1
< 0.1%
85.64996278 1
< 0.1%
85.11396424 1
< 0.1%
80.14542259 1
< 0.1%
79.49180149 1
< 0.1%
79.37429998 1
< 0.1%
78.9208821 1
< 0.1%
77.99585588 1
< 0.1%
77.61198204 1
< 0.1%

Interactions

2024-07-05T16:32:13.510061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:50.780987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:58.649245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:01.172764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:04.216701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:06.419252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:08.581783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:10.956815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:13.961267image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:51.348957image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:58.973983image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:01.624770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:04.549265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:06.771833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:08.753418image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:11.162256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:14.220149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:51.994137image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:59.233810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:02.282451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:04.928776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:07.191256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:09.205562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:11.446750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:14.510035image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:52.565346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:59.452178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:02.659207image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:05.274165image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:07.444348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:09.566823image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:11.724087image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:14.837678image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:53.204834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:59.736733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:02.859475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:05.557673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:07.648565image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:09.894479image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:12.200313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:15.170720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:53.482990image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:00.089159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:03.186610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:05.746130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:07.852045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:10.090895image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:12.548997image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:15.451497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:53.746672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:00.355172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:03.565546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:05.954930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:08.086203image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:10.421516image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:12.859231image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:15.742643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:31:54.205228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:00.772049image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:03.885365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:06.203410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:08.308493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:10.638788image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T16:32:13.163483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T16:32:26.697292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
per_activeper_area_buildingsper_area_greeneryper_area_waterper_highwayper_residential_roadper_rural_roadpublictransport_frequency
per_active1.000-0.1360.0960.1790.018-0.433-0.0690.158
per_area_buildings-0.1361.000-0.879-0.061-0.2790.749-0.7650.148
per_area_greenery0.096-0.8791.000-0.0430.264-0.7230.761-0.130
per_area_water0.179-0.061-0.0431.0000.056-0.070-0.0550.057
per_highway0.018-0.2790.2640.0561.000-0.3160.2170.039
per_residential_road-0.4330.749-0.723-0.070-0.3161.000-0.7670.082
per_rural_road-0.069-0.7650.761-0.0550.217-0.7671.000-0.146
publictransport_frequency0.1580.148-0.1300.0570.0390.082-0.1461.000

Missing values

2024-07-05T16:32:16.269535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T16:32:17.032059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

publictransport_frequencyper_area_greeneryper_area_waterper_area_buildingsper_residential_roadper_rural_roadper_highwayper_active
04388.00.6084141.82235914.61667688.4077740.0000000.00000011.592226
10.035.1565580.2313780.1942761.71652861.44967029.8227557.011047
22402.031.3413770.7030570.2821070.00000096.2316650.0000003.768335
30.025.4263221.2555171.06205439.1351090.0000000.00000060.864891
42185.06.3597280.0311465.12882882.91230415.5156540.0000001.572042
50.00.0517550.00000017.25975299.9167950.0000000.0000000.083205
6165.010.2343480.5740086.46145179.7189420.0000000.00000020.281058
70.022.7527100.0627581.42580526.84463173.1553690.0000000.000000
80.01.93112819.8999354.51828987.3642150.0000000.00000012.635785
91615.016.4522500.7150293.71835283.9847663.1320683.0975009.785666
publictransport_frequencyper_area_greeneryper_area_waterper_area_buildingsper_residential_roadper_rural_roadper_highwayper_active
127340.00.0000000.00000012.89535088.9978670.0000000.00000011.002133
12735572.07.7947361.9668177.61015380.5880650.0000000.00000019.411935
127361433.07.7406941.7291085.59119075.6475360.0000002.58632921.766136
127372340.02.0152801.3649305.68670472.4736110.8880830.00000026.638306
12738360.05.1188411.9357035.33914286.8045835.0719410.0000008.123476
127394350.016.7912402.7362152.67583050.97655415.76236810.83224422.428835
127400.011.2396930.4515017.04651491.8512700.0000000.0000008.148730
127414566.03.88420113.9790723.96065874.5580541.5702570.00000023.871690
127420.012.1906092.1729694.92254294.9966570.0000000.0000005.003343
127430.021.2493080.2079710.2737231.20900971.40695018.9392948.444747